Book Image

Jupyter for Data Science

By : Dan Toomey
Book Image

Jupyter for Data Science

By: Dan Toomey

Overview of this book

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create documents that contain live code, equations, and visualizations. This book is a comprehensive guide to getting started with data science using the popular Jupyter notebook. If you are familiar with Jupyter notebook and want to learn how to use its capabilities to perform various data science tasks, this is the book for you! From data exploration to visualization, this book will take you through every step of the way in implementing an effective data science pipeline using Jupyter. You will also see how you can utilize Jupyter's features to share your documents and codes with your colleagues. The book also explains how Python 3, R, and Julia can be integrated with Jupyter for various data science tasks. By the end of this book, you will comfortably leverage the power of Jupyter to perform various tasks in data science successfully.

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Table of Contents (17 chapters)
Title Page
Title Page
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Jupyter and Data Science
Jupyter and Data Science
Jupyter concepts
A first look at the Jupyter user interface
Summary
2
Working with Analytical Data on Jupyter
Working with Analytical Data on Jupyter
Data scraping with a Python notebook
Using heavy-duty data processing functions in Jupyter
Using SciPy in Jupyter
Expanding on panda data frames in Jupyter
Summary
3
Data Visualization and Prediction
Data Visualization and Prediction
Make a prediction using scikit-learn
Make a prediction using R
Interactive visualization
Plotting using Plotly
Creating a human density map
Draw a histogram of social data
Plotting 3D data
Summary
4
Data Mining and SQL Queries
Data Mining and SQL Queries
Special note for Windows installation
Using Spark to analyze data
Another MapReduce example
Using SparkSession and SQL
Combining datasets
Loading JSON into Spark
Using Spark pivot
Summary
5
R with Jupyter
R with Jupyter
How to set up R for Jupyter
R data analysis of the 2016 US election demographics
Analyzing 2016 voter registration and voting
Analyzing changes in college admissions
Predicting airplane arrival time
Summary
6
Data Wrangling
Data Wrangling
Reading a CSV file
Reading another CSV file
Manipulating data with dplyr
Sampling a dataset
Tidying up data with tidyr
Summary
7
Jupyter Dashboards
Jupyter Dashboards
Visualizing glyph ready data
Publishing a notebook
Creating a Shiny dashboard
Building standalone dashboards
Summary
8
Statistical Modeling
Statistical Modeling
Converting JSON to CSV
Evaluating Yelp reviews
Using Python to compare ratings
Visualizing average ratings by cuisine
Arbitrary search of ratings
Determining relationships between number of ratings and ratings
9
Machine Learning Using Jupyter
Machine Learning Using Jupyter
Naive Bayes
Nearest neighbor estimator
Decision trees
Neural networks
Random forests
Summary
10
Optimizing Jupyter Notebooks
Optimizing Jupyter Notebooks
Deploying notebooks
Optimizing your script
Monitoring Jupyter
Caching your notebook
Securing a notebook
Scaling Jupyter Notebooks
Sharing Jupyter Notebooks
Converting a notebook
Versioning a notebook
Summary

Building standalone dashboards

Using Node.js, developers have come up with a way to host your dashboard/notebook without Jupyter on jupyter-dashboard-server.

Installation requires installing Node.js (as the server is written in Node.js). This is a larger installation set.

Once you have Node.js installed, one of the tools installed is npm-node product manager. You can use npm to install the dashboard server with the following command:

npm install -g jupyter-dashboards-server

Once installed you can run the server with the following command:

C:\Users\Dan>jupyter-dashboards-server --KERNEL_GATEWAY_URL=http://my.gateway.com

mygateway.com is a dummy. You would use your gateway server (if needed). At this point the server is running on the environment you mentioned and will output a few lines:

Using generated SESSION_SECRET_TOKENJupyter dashboard server listening on 127.0.0.1:3000

You can open a browser to the URL (http://127.0.0.1:3000/dashboards) and see what the server console looks like:

As for developing...

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